import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.distributed as dist
from .config import ModelArgs
from ..kernel import act_quant, weight_dequant, fp8_gemm

class MLA(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.dim = args.dim
        self.n_heads = args.n_heads
        self.n_local_heads = args.n_heads // dist.get_world_size() if dist.is_initialized() else args.n_heads
        self.q_lora_rank = args.q_lora_rank
        self.kv_lora_rank = args.kv_lora_rank
        self.qk_nope_head_dim = args.qk_nope_head_dim
        self.qk_rope_head_dim = args.qk_rope_head_dim
        self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim
        self.v_head_dim = args.v_head_dim
        
        # Initialize components (implementation from original MLA class)
        # ... (rest of the MLA implementation)

    def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
        # ... (MLA forward implementation)